AI & Philosophy student at La Sapienza · Founder-in-progress of AIthos · FDE-oriented AI engineer

_{Forward Deployed AI Engineer: a role focused on working close to real organizations, identifying where AI can create impact, redesigning workflows around it, and turning prototypes into durable systems.}

I’m Riccardo, an AI & Philosophy student at La Sapienza University of Rome, currently building my path between AI engineering, philosophy of technology, applied automation and real-world business workflows.

This GitHub is my personal technical space.

It collects my university projects, thesis work, research experiments, applied AI prototypes and early systems connected to AIthos, the AI consultancy project I’m building for SMEs.

AIthos starts from a simple idea:

Applied AI should reduce repetitive work and free human intelligence for higher-value tasks.

I’m currently completing my undergraduate degree in Philosophy & Artificial Intelligence at La Sapienza University of Rome.

My next academic goal is to continue toward a more engineering-oriented path through the Master’s Degree in Ingegneria dei Sistemi Intelligenti (LM-32) at Università Campus Bio-Medico di Roma.

My direction is clear: I want to combine philosophical understanding of AI with the technical ability to build real systems, evaluate models and deploy useful automation in concrete contexts.

My current work is focused on LLM agents, model evaluation and applied AI systems.

The main research direction is a thesis project on LLMs as decision-making agents in a trading environment, with a comparative approach across different models.

The thesis is structured around three main dimensions:

• Feasibility — whether LLMs can act as trading decision-making agents in a controlled experimental environment.
• Explainability — whether their decisions can be logged, reconstructed and interpreted through transparent reasoning traces.
• Context-emergent properties — what behavioral patterns emerge when different models operate over time under the same shared context.

I’m also part of Simone Rizzo’s AI Academy, a practical AI engineering community founded by Simone Rizzo, an AI engineer, contract lecturer at the University of Bologna and founder of Inferentia, focused on applied AI, agents, automation and real-world AI workflows.

AIthos is my founder-in-progress project: an applied AI consultancy for SMEs.

The goal is not simply to “add AI” inside companies.

The goal is to understand how a company works, identify repetitive and low-value tasks, and redesign workflows so that humans can move from manual execution to supervision, judgment, creativity and strategic direction.

AIthos aims to help companies and people move from repetitive execution to intelligent direction.

The long-term vision is a work environment where humans do not disappear from the process, but learn to govern intelligent systems: defining goals, supervising agents, validating outputs, making decisions and giving direction.

In this sense, AI is not only a tool for automation.

It is a way to help humans relearn how to work with more clarity, responsibility and creative freedom.

• process analysis
• AI workflow design
• automation of repetitive tasks
• internal dashboards
• document and knowledge workflows
• human-in-the-loop systems
• AI agents for business operations
• practical AI adoption for SMEs

One of the first business contexts I’m studying is Martina RE, an Italian real estate asset & development management company with operations connected to financial management, project and construction management, strategic marketing, interior design, property management and sales management.

The goal of this case study is to understand how an AI consultancy can work inside a real SME environment by:

• mapping existing workflows
• identifying repetitive operational tasks
• analyzing documents, procedures and business processes
• designing possible AI automations
• building internal tools and dashboards
• testing how AI can reduce friction and improve clarity
• translating business needs into technical systems

This case study is also a way to shape the future service model of AIthos: practical, grounded, process-aware and focused on real business value.

• AI-Agent-for-Trading
  LLM trading agent backend for thesis research and experimental decision-making systems.

• AI-Agent-for-Trading-Dashboard
  Dashboard for monitoring agent decisions, market context, trading behavior and experimental results.

• Soybean-Dataset-Project
  University Machine Learning project for the course Intelligenza Artificiale 2 at La Sapienza.
  The project applies supervised regression models to predict soybean grain yield from agronomic and morphological features, with a reproducible scikit-learn pipeline and model comparison.

This section will collect future personal repositories related to:

• AI agents
• local and cloud model workflows
• AI-assisted development
• automation experiments
• research notes
• model evaluation
• applied ML systems
• technical learning projects

These repositories will represent my personal technical growth and my attempt to turn study, experimentation and research into working systems.

This section will collect future public repositories connected to AIthos and to applied AI case studies.

Possible areas:

• SME workflow analysis
• business process automation
• internal AI dashboards
• document intelligence systems
• company knowledge bases
• AI-assisted reporting
• human-in-the-loop tools
• applied agents for business operations

The goal is to progressively build a public technical trace of how AIthos evolves from idea to practice.

I’m currently strengthening my skills in building real AI-enabled systems: backend services, databases, APIs, dashboards, agent workflows and deployment-ready prototypes.

Current stack and tools:

Python · SQLAlchemy · PostgreSQL · FastAPI/Flask · scikit-learn · Jupyter · TypeScript · Astro · Tailwind CSS · LLM APIs · Claude Code · OpenRouter · local models · WSL/Linux

I don’t see AI only as a way to make work faster.

I see it as a way to rethink how work is organized, how intelligence is distributed between humans and systems, and how people can move from repetitive execution to higher-level direction.

A parallel goal of mine is to keep studying these systems through scientific papers, technical research and philosophical reflection.

Not only for business applications, but also to understand their effects on the world: how AI changes workflow, learning, perception, decision-making, human agency and the future evolution of intelligent systems.

In the future, I plan to create a newsletter dedicated to this intersection:

engineering papers, applied AI systems and occasional philosophical reflections on the impact of artificial intelligence.

Newsletter: coming soon